Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

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conception

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,Anomaly Detection in Directed Dynamic Graphs via RDGCN and LSTAN, deep learning-based methods often overlook the asymmetric structural characteristics of directed dynamic graphs, limiting their applicability to such graph types. Furthermore, these methods inadequately consider the long-term and short-term temporal features of dynamic graphs, which hampers their a

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,Anomaly-Based Insider Threat Detection via Hierarchical Information Fusion,in recent years. Anomaly-based methods are one of the important approaches for insider threat detection. Existing anomaly-based methods usually detect anomalies in either the entire sample space or the individual user space. However, we argue that whether the behavior is anomalous depends on the cor

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,CSEDesc: CyberSecurity Event Detection with Event Description,ty analysis. However, previous approaches considered it as a trigger classification task, which has limitations in accurately locating triggers, especially for long phrases commonly used in the cybersecurity domain. Additionally, tagging triggers is often time-consuming and unnecessary. To address t

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,K-Fold Cross-Valuation for Machine Learning Using Shapley Value,aining set by using the model’s performance on a validation set as a utility function. However, since the validation set is often a small subset of the complete dataset, a dataset shift between the training and validation sets may lead to biased data valuation. To address this issue, this paper prop

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,Time Series Anomaly Detection with Reconstruction-Based State-Space Models,rations. Identifying abnormal data patterns and detecting potential failures in these scenarios are important yet rather challenging. In this work, we propose a novel anomaly detection method for time series data. The proposed framework jointly learns the observation model and the dynamic model, and